Overview Use Cases Customer Churn Prediction Time Series Anomaly Detection Event Anomaly Detection Cloud Spend Alerts Personalized Promotions Predictive Modeling Real-Time Forecasting Financial Metrics Forecasting Demand Forecasting Cumulative Forecasting Text extraction and classification NLP Powered Search Sentiment Analysis Finetuned LLM ChatLLM Language Detection Image Classification & Detection Object Detection Clustering Timeseries Clustering Sales and Revenue Forecasting Predictive Lead Scoring Personalized Search Personalized Recommendations Related Items Model Drift and Monitoring Tensorflow with Vector Matching Custom Python Model Data Ingestion Streaming Feature Store Vector Store Updating the Embeddings Index Predictions AI Agents Named Entity Recognition Guidelines Optimization Connectors Authentication Getting Started with the Python SDK API Documentation Chat Bot API Search How to

Vector Store

Provide your embeddings and we'll provide fast scalable nearest neighbor search over them. Vector Store use case is divided into the following sections:

Updating Embedding Index

Abacus.AI has specialized infrastructure to do fast K-Nearest Neighbor search on a set of embeddings. Our search system provides scalable exact search returning top-K (k can be specified at query time) supporting various distance functions. We provide a simple REST API to do the search (that supports specifying k and the distance function) and API's to update your embeddings index.

Prediction API

This section contains the API method reference that could be used to query your deployed embeddings index.